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Subject: search.filters.subject.Medical image segmentation

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    Study and implementation of u-net encoder-decoder neural network for brain tumors segmentation
    (Springer, 2023) Cherifi, Dalila; Bekkouche, Abdelghani; Bayou, Meroua; Benmalek, Assala; Mechti, Ines; Bekkour, Belkacem; Amine, Chaima; Ahmed, Halak
    Emerging advanced technologies have seen a revolution of applications into medical field, in all its aspects and sides, this has helped healthcare practitioners and empowered them in achieving accurate diagnosis and treatment, specifically with the evolution of computer Aided Diagnosis systems which use image processing techniques, Computer vision,and deep learning applied on different medical images in order to diagnose the image, or sections of the image with particular diseases or illnesses. Medical images of multiples organs or parts of the body (Liver, brain, kidney, skin, etc..) can today be visualized thanks to the advanced medical imaging techniques that exists in the market (MRI, CT, etc…) these technologies uses high energy in order to acquire high quality images but high energy can harm human cells, this is why we us low energy and with this used we get slightly low quality medical images, and here technology intervenes where we can use preprocessing techniques in order to increase image resolution prior to perform diagnosis either by doctor or CAD system. We present in this paper a computer aided diagnosis system that provides an automated brain tissue segmentation applied on 3D MRI images with its four different modalities (T1, T1C, T2, T2 weighted) of BRatS 2020 challenge dataset, by implementing a U-Net like deep neural network which provides information about classification of brain tissue into healthy tissue, Edema, Enhancing tumour, Non enhancing tumour. The model achieved an accuracy of 99.01% and dice coefficient of 47.95% after 35 epochs of training
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    A review of recent progress in deep learning-based methods for MRI brain tumor segmentation
    (Institute of Electrical and Electronics Engineers Inc, 2020) Chihati, S.; Gaceb, Djamel
    Brain tumor segmentation is a challenging task that involves delimiting cancerous tissues with heterogeneous and diffuse forms in brain medical images. This process is undoubtedly an important step in computer-aided diagnosis systems, in which tumor regions must be isolated for visualization and subsequent analysis. Recently, great progress has been made in brain tumor segmentation with the emergence of deep learning-based methods, which automatically learn hierarchical, and discriminative features from raw data. These methods outperformed the classical machine learning approaches where handcrafted features are used to describe the differences between pathological and healthy tissues. In this paper, we present a comprehensive overview of recent progress in deep learning-based methods for brain tumor segmentation from magnetic resonance images. Moreover, we discuss the most common challenges and suggest possible solutions